Quadratic regularization design for fan beam transmission tomography

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dc.contributor.author Shi, Hugo en_US
dc.contributor.author Fessler, Jeffrey A. en_US
dc.date.accessioned 2011-08-18T18:21:09Z
dc.date.available 2011-08-18T18:21:09Z
dc.date.issued 2005-02-17 en_US
dc.identifier.citation Shi, H.; Fessler, J. A. (2005). "Quadratic regularization design for fan beam transmissiontomography." Proc. Of SPIE. Medical Imaging: Image Processing: 5747: 2023-2033. <http://hdl.handle.net/2027.42/85936> en_US
dc.identifier.uri http://hdl.handle.net/2027.42/85936
dc.description.abstract Statistical methods for tomographic image reconstruction have shown considerable potential for improving image quality in X-ray CT. Penalized-likelihood (PL) image reconstruction methods require maximizing an objective function that is based on the log-likelihood of the sinogram measurements and on a roughness penalty function to control noise. In transmission tomography, PL methods (and MAP methods) based on conventional quadratic regularization functions lead to nonuniform and anisotropic spatial resolution, even for idealized shift-invariant imaging systems. We have previously addressed this problem for parallel-beam emission tomography by designing data-dependent, shift-variant regularizers that improve resolution uniformity. This paper extends those methods to the fan-beam geometry used in X-ray CT imaging. Simulation results demonstrate that the new method for regularization design requires very modest computation and leads to nearly uniform and isotropic spatial resolution in the fan-beam geometry when using quadratic regularization. en_US
dc.publisher SPIE en_US
dc.title Quadratic regularization design for fan beam transmission tomography en_US
dc.type Article en_US
dc.subject.hlbsecondlevel Biomedical Engineering en_US
dc.subject.hlbtoplevel Engineering en_US
dc.description.peerreviewed Peer Reviewed en_US
dc.contributor.affiliationum EECS Department en_US
dc.description.bitstreamurl http://deepblue.lib.umich.edu/bitstream/2027.42/85936/1/Fessler208.pdf
dc.identifier.doi 10.1117/12.593412 en_US
dc.identifier.source Proc. Of SPIE. Medical Imaging: Image Processing en_US
dc.owningcollname Electrical Engineering and Computer Science, Department of (EECS)
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